Approximate Nearest Neighbors : Data Science Concepts

[@ScaredCrows4]

Wow you have no idea how much i needed this for my current work project. Thanks as always for a fantastic explanation

[@vineethgudela2033]

I have implemented ANN on my own after watching your video. Thanks for the great explanation ritvik

[@anonim5052]

I am preparing for pinterest interview! Thank you! It was very helpful!

[@zivleibowitz9846]

OMG. I hope all my lecturers will explain that clearly and intuitively. Thankss

[@rockapedra1130]

You have a very clear but not too wordy style. *SUBSCRIBED*

[@csbanki]

This is perfect! I'm so sick of all these fancy literatury stuff from professors all over the world who can only communicate through differential equations. THIS is how it should be explained. Thank you good sir!

[@Septumsempra8818]

Both formats are cool

[@jiayangcheng]

Thank you so much sir this explanation shows your exceptional ability to teach. So enlightening!

[@hannahnelson4569]

This is brilliant! Thank you so much for showing us this method!

[@aZnPriDe707]

Clear explanation and very resourceful!

[@ctRonIsaac]

The lesson was clear and paper can be easier for you to control and work with. So this is fine. Thank you for the lesson!

[@Mci146]

Thank you so much for the simple and clear explanation with examples!

[@ritvikmath]

You're very welcome!

[@seyedalirezaabbasi]

This format is better. Thanx.

[@Crimau12000]

Thanks for sharing such a detaild and thorough explanation!

[@ritvikmath]

My pleasure!

[@randall.chamberlain]

Mate you really know how to explain things. Thanks for your time and dedication.

[@marcosricardooliveira3790]

I really like this format for this kind of explanation Like explainnig how a technique works very good vid, thanks

[@ritvikmath]

Glad you liked it!

[@monalover3758]

Very clear explanation! I think I got it in one pass! Pace is good. Thanks! (PS. the paper format is fine!)

[@RetropunkAI]

best explanation ever. thank you

[@siddharthvij9087]

Excellent Video

[@ritvikmath]

Glad you enjoyed it!

[@doronbenchayim8526]

THIS WAS AMAZING!!!!!!!!!!!!!!!

[@vinnythep00h]

Great explanation!

[@ritvikmath]

Glad it was helpful!

[@oddtraveller]

Greatly explained

[@dinuthomas4531]

Very well explained!!

[@ritvikmath]

Glad you think so!

[@PhilipMavrepis]

Pretty good explanation but you never showed what happens if the number of K you are searching for is bigger than the number of points in the specific area. For example let's say you have a new point in R4 which has 3 points and you are searching for 4-NN for that point. Thank you again for this video, really liked it

[@Han-ve8uh]

Doesn't answer your question directly, but in FAISS IVF index, if k is more than number of items in a cell, it returns -1 id for the extra required neighbors, solution is to increase default nprobe=1 to probe more cells.

[@KeithGalli]

Thanks! Good vid :)

[@zahrashekarchi6139]

well explained! thanks!

[@alinajafi1528]

Perfect explanation! Thanks :D

[@nadiakacem24]

thank you very much, it was so helpful

[@Shkedias]

Thank you😊

[@jamemamjame]

thank you very much 🙏🏼

[@ritvikmath]

Of course!

[@sarmale-cu-mamaliga]

Really cool :O thank you

[@mayapankhaj9124]

Thank you so much for a beautiful lesson. Reminded me of my elementary school days and how teachers used to teach back then.

[@qwerty22488]

Thanks for this excellent video! Is there a poplar library that helps to experiment with ANN on local machine for a small set of data?

[@RishiRajKoul]

How would we determine that a point is above and below a line using code ?

[@haneulkim4902]

Thanks for a great video! One questions, @9:23 new point we check if given point is below or above the blue line. The way you recognize whether point is above or below is by calculating distance between (point, 1) and (point, 9) ?

[@brockobama257]

3:56 I thought that a kdtree can search nearest neighbor in logn and delete or add a point in logn so k nearest neighbors could be considered klogn which is less than n

[@loupax]

I now wonder if this is a sensible algorithm for collision detection

[@kanchansarkar7706]

such a great explanation! Wonder do you also have a similar video for HNSW? Thanks!

[@ScottSummerill]

I like it MUCH better. I found it sometimes overwhelming to be confronted with all the info and not yet have an explanation.

[@JayRodge]

I still don't understand how do you classify the new point? region wise or is there any other method?

[@monalover3758]

Here is what I think: each region has two points. So use a metrics (e.g. distance) from this given new point to the begin and to the end point and go with the closer one. The closeness can be Euclidean distance, or Cosine distance, or some other metrices.

[@stevengusenius7333]

Great video as always Ritvik. Am I correct that building the tree is an O(N) operation? That is, if I have only one new data point and haven't yet constructed the tree, will this still save any time over the exhaustive method? If not, then I presume building a forest would imply some break even point. Thanks.

[@rockapedra1130]

Paper is better, I think. Moving the papers around is like zooming without moving the camera.

[@djangoworldwide7925]

Such a nice recursive challenge. anyone have an idea how to define a function to recursivley solve this kind of algorithm, given a creiteria of maximum points?

[@borknagarpopinga4089]

What's your qualification? Somehow I cannot find any information about your education etc. Awesome videos by the way, a lot easier to understand than what every professor tries to explain.

[@atwork22]

Looks like a sort of a binary search

[@X_platform]

Is ANNOY using Voronoi ?

[@raunaquepatra3966]

"Lowest Complexity for Knn is O(n)" is not True!! Using kd-tree the complexity becomes O(log n).

[@jasdeepsinghgrover2470]

I was also thinking about kd tree and ball tree used in sklearn... Are you aware of any other methods??

[@raunaquepatra3966]

@@jasdeepsinghgrover2470 LSH

[@jasdeepsinghgrover2470]

Thanks for sharing... Will learn more about it as well